{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import folium\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc(\"font\", family = 'Malgun Gothic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r\"C:\\\\df_merge_2018.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Min_Max standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    metropolis and province district_kor                    district  \\\n",
      "0                       강원도          강릉시                Gangneung-si   \n",
      "1                       강원도          고성군                 Goseong-gun   \n",
      "2                       강원도          동해시                  Donghai-si   \n",
      "3                       강원도          삼척시                 Samcheok-si   \n",
      "4                       강원도          속초시                   Sokcho-si   \n",
      "..                      ...          ...                         ...   \n",
      "245                    충청북도      청주시 상당구   Sangdang-gu (Cheongju-si)   \n",
      "246                    충청북도      청주시 서원구     Seowon-gu (Cheongju-si)   \n",
      "247                    충청북도      청주시 청원구  Cheongwon-gu (Cheongju-si)   \n",
      "248                    충청북도      청주시 흥덕구  Heungdeok-gu (Cheongju-si)   \n",
      "249                    충청북도          충주시                  Chungju-si   \n",
      "\n",
      "     price_per_area  land_inequality   longitude   latitude  \\\n",
      "0       6855.725904         0.014222  128.878497  37.749136   \n",
      "1       3326.593497         0.011996  128.470164  38.377961   \n",
      "2       9567.690932         0.006230  129.116633  37.521931   \n",
      "3       1436.066956         0.004233  129.167489  37.447086   \n",
      "4      26658.905919         0.014359  128.594167  38.204275   \n",
      "..              ...              ...         ...        ...   \n",
      "245    14197.986447         0.012608  127.506604  36.590837   \n",
      "246    54280.530877         0.015640  127.469770  36.639450   \n",
      "247    32385.800180         0.012579  127.486656  36.651721   \n",
      "248    51415.273167         0.022847  127.376058  36.630266   \n",
      "249     8670.556095         0.012925  127.894584  37.014951   \n",
      "\n",
      "     scaled_price_per_area  scaled_land_inequality  \n",
      "0                -0.997121               -0.980749  \n",
      "1                -0.998787               -0.984186  \n",
      "2                -0.995840               -0.993085  \n",
      "3                -0.999680               -0.996167  \n",
      "4                -0.987769               -0.980537  \n",
      "..                     ...                     ...  \n",
      "245              -0.993653               -0.983241  \n",
      "246              -0.974724               -0.978561  \n",
      "247              -0.985064               -0.983284  \n",
      "248              -0.976078               -0.967437  \n",
      "249              -0.996264               -0.982750  \n",
      "\n",
      "[250 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "## Define the scaling function\n",
    "def scale_to_minus_one_one(x, x_min, x_max):\n",
    "    scaled_value = ((x - x_min) / (x_max - x_min)) * 2 - 1\n",
    "    return scaled_value\n",
    "\n",
    "##Define the min and max of the original ranges\n",
    "original_min_price = df['price_per_area'].min()\n",
    "original_max_price = df['price_per_area'].max()\n",
    "\n",
    "original_min_inequality = df['land_inequality'].min()\n",
    "original_max_inequality = df['land_inequality'].max()\n",
    "\n",
    "##Apply the scaling function to the columns\n",
    "df['scaled_price_per_area'] = df['price_per_area'].apply(lambda x: scale_to_minus_one_one(x, original_min_price, original_max_price))\n",
    "df['scaled_land_inequality'] = df['land_inequality'].apply(lambda x: scale_to_minus_one_one(x, original_min_inequality, original_max_inequality))\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('district')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 여러개의 클러스터링 갯수를 List로 입력 받아 각각의 실루엣 계수를 면적으로 시각화한 함수 작성\n",
    "def visualize_silhouette(cluster_lists, X_features): \n",
    "    \n",
    "    from sklearn.datasets import make_blobs\n",
    "    from sklearn.cluster import KMeans\n",
    "    from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    import matplotlib.cm as cm\n",
    "    import math\n",
    "    \n",
    "    # 입력값으로 클러스터링 갯수들을 리스트로 받아서, 각 갯수별로 클러스터링을 적용하고 실루엣 개수를 구함\n",
    "    n_cols = len(cluster_lists)\n",
    "    \n",
    "    # plt.subplots()으로 리스트에 기재된 클러스터링 수만큼의 sub figures를 가지는 axs 생성 \n",
    "    fig, axs = plt.subplots(figsize=(4*n_cols, 4), nrows=1, ncols=n_cols)\n",
    "    \n",
    "    # 리스트에 기재된 클러스터링 갯수들을 차례로 iteration 수행하면서 실루엣 개수 시각화\n",
    "    for ind, n_cluster in enumerate(cluster_lists):\n",
    "        \n",
    "        # KMeans 클러스터링 수행하고, 실루엣 스코어와 개별 데이터의 실루엣 값 계산. \n",
    "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0)\n",
    "        cluster_labels = clusterer.fit_predict(X_features)\n",
    "        \n",
    "        sil_avg = silhouette_score(X_features, cluster_labels)\n",
    "        sil_values = silhouette_samples(X_features, cluster_labels)\n",
    "        \n",
    "        y_lower = 10\n",
    "        axs[ind].set_title('Number of Cluster : '+ str(n_cluster)+'\\n' \\\n",
    "                          'Silhouette Score :' + str(round(sil_avg,3)), fontsize=16 )\n",
    "        axs[ind].set_xlabel(\"The silhouette coefficient values\", fontsize=13)\n",
    "        axs[ind].set_ylabel(\"Cluster label\")\n",
    "        axs[ind].set_xlim([-0.1, 1])\n",
    "        axs[ind].set_ylim([0, len(X_features) + (n_cluster + 1) * 10])\n",
    "        axs[ind].set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        axs[ind].set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "        \n",
    "        # 클러스터링 갯수별로 fill_betweenx( )형태의 막대 그래프 표현. \n",
    "        for i in range(n_cluster):\n",
    "            ith_cluster_sil_values = sil_values[cluster_labels==i]\n",
    "            ith_cluster_sil_values.sort()\n",
    "            \n",
    "            size_cluster_i = ith_cluster_sil_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "            \n",
    "            color = cm.nipy_spectral(float(i) / n_cluster)\n",
    "            axs[ind].fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_sil_values, \\\n",
    "                                facecolor=color, edgecolor=color, alpha=0.7)\n",
    "            axs[ind].text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "            y_lower = y_upper + 10\n",
    "            \n",
    "        axs[ind].axvline(x=sil_avg, color=\"red\", linestyle=\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 여러개의 클러스터링 갯수를 List로 입력 받아 각각의 클러스터링 결과를 시각화 \n",
    "def visualize_kmeans_plot_multi(cluster_lists, X_features):\n",
    "    \n",
    "    from sklearn.cluster import KMeans\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    \n",
    "    # plt.subplots()으로 리스트에 기재된 클러스터링 만큼의 sub figures를 가지는 axs 생성 \n",
    "    n_cols = len(cluster_lists)\n",
    "    fig, axs = plt.subplots(figsize=(4*n_cols, 5), nrows=1, ncols=n_cols)\n",
    "    \n",
    "    \n",
    "     # 리스트에 기재된 클러스터링 갯수들을 차례로 iteration 수행하면서 KMeans 클러스터링 수행하고 시각화\n",
    "    for ind, n_cluster in enumerate(cluster_lists):\n",
    "        \n",
    "        # KMeans 클러스터링으로 클러스터링 결과를 dataframe에 저장. \n",
    "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0)\n",
    "        cluster_labels = clusterer.fit_predict(df)\n",
    "        df['kmeans_label']=cluster_labels\n",
    "        \n",
    "        unique_labels = np.unique(clusterer.labels_)\n",
    "        markers=['o', 's', '^', 'x', '*', '>', '<', '+']\n",
    "\n",
    "        \n",
    "        # 클러스터링 결과값 별로 scatter plot 으로 시각화\n",
    "        for label in unique_labels:\n",
    "            label_df = df[df['kmeans_label']==label]\n",
    "            if label == -1:\n",
    "                cluster_legend = 'Noise'\n",
    "            else :\n",
    "                cluster_legend = 'Cluster '+str(label)           \n",
    "            axs[ind].scatter(x=label_df['price_per_area'], y=label_df['land_inequality'], s=70,\\\n",
    "                        edgecolor='k', marker=markers[label], label=cluster_legend)\n",
    "        \n",
    "\n",
    "        axs[ind].set_title('Number of Cluster : '+ str(n_cluster), fontsize=16)    \n",
    "        axs[ind].legend(loc='lower right')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_silhouette([2,3,4,5,6],df)\n",
    "visualize_kmeans_plot_multi([2,3,4,5,6],df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
